Abstract
Tea is one of the most important beverage in India. It is the number one foreign exchange earner. India is the largest producer of tea in the world. The Indian states of Assam, Meghalaya, Tripura, North Bengal (Darjeeling) and Sikkim contribute significantly to the overall tea production in the country. Apart from those, South Indian states of Tamil Nadu, Karnataka and Kerala also contribute to the production of tea.
Over a past few years, it was found that the tea industry is loosing it’s ground. This is mainly because of wrong production mix, inability to compete with other tea producing countries due to high cost of production, organization of small holder farmers, poor quality control at the processing level and more significantly from pests and disease infestations.
Remote sensing and GIS technologies have been efficiently used for monitoring several annual crops like rice, wheat, etc. Therefore, developing an approach for monitoring tea plantations using remote sensing and GIS has become a pressing need. The lack of previous studies in monitoring tea using remote sensing provided the idea to develop an approach that can aid in monitoring the growth of plantations and help in taking effective measures when the need arises.
In this study, an attempt has been made to assess the tea bush health using texture and tonal variations from remotely sensed images. The Grey Level Co-occurrence Matrix (GLCM) technique was applied to categorize the tea patches into healthy, moderately healthy and diseased tea. The diseased patches were delineated using both texture and the classified images. The percentage of healthy, moderately healthy and diseased tea was derived. It was observed that LANDSAT image of December, 2001 showed 60.4% area under healthy tea, 23.6% area under moderately affected tea and 16.2% area under diseased tea. For the LISS III image of February, 2004, it was found that 43.9% area under healthy tea, 36.8% area under moderately affected tea and 19.3% area under diseased tea. Similarly for ASTER image of June, 2004, area under healthy tea was found to be 24.9%, moderately healthy tea was found to be 50.1% and diseased tea to be 25.1%.The results were finally compared with the ground Leaf Area Index (LAI) and the yield. Thus the texture analysis and tonal variations attempted here could play an important role in identifying and detecting disease patches in tea plantations.
Further to test whether MODIS derived NDVI is related to LAI, an empirical equation was established which shows that LAI in tea had significant and linear relationship with NDVI (R2=0.36). This study showed that MODIS based NDVI during April, June and August was significantly correlated to tea leaf yield at estate level. However it was found that NDVI observation at different time period alone could not explained much variance in tea leaf yield. This shows that statistical model for tea yield does not seem to be encouraging.